Data were negcon normalized
### Modules import
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.io as pio
import pandas as pd
combined_moa_cellcount_df = pd.read_csv('copairs_csv\\PrecisionValues_with_MoA_allplates_cellcount_Negcon_wrt_Controls.csv')
combined_moa_df = pd.read_csv('copairs_csv\\PrecisionValues_with_MoA_allplates_Negcon_wrt_Controls.csv')
actin_fig = px.scatter(combined_moa_cellcount_df, x =combined_moa_cellcount_df['average_precision_std'], y=combined_moa_cellcount_df['average_precision_act'],labels={'average_precision_std':'Mean Average Precision - Standard CellPainting dyes', 'average_precision_act':'Mean Average Preicison - <br> Phalloidin 400LS (long-stoke shifted)'}, color=combined_moa_cellcount_df['MoA'])
actin_fig.update_layout(legend=dict(orientation="h"), height=800, width=1000)
actin_fig.show('notebook')
combined_box_plot = go.Figure()
combined_box_plot.add_trace(go.Box(y=combined_moa_df['average_precision_std'], name = 'Standard Cellpainting dyes', boxpoints='all', hovertext=combined_moa_df['MoA']+'-'+ combined_moa_df['Common Name']))
combined_box_plot.add_trace(go.Box(y=combined_moa_df['average_precision_act'], name = 'Phalloidin 400LS', boxpoints='all', hovertext=combined_moa_df['MoA']+'-'+ combined_moa_df['Common Name']))
combined_box_plot.update_layout(height=800,width=1000, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision')
combined_box_plot.show('notebook')
The size of the markers represent the average number of cells present in the replicates. The number of cells were normalized by dividing the actual number by 100 for easier plotting.
scatter_plot = go.Figure()
scatter_plot.add_trace(go.Scatter(x=combined_moa_cellcount_df['MoA'], y=combined_moa_cellcount_df['average_precision_std'],hovertext=[combined_moa_cellcount_df['Metadata_Count_Cells_Std']], mode='markers', name = 'Standard Cellpainting dyes', marker_size =combined_moa_cellcount_df['Metadata_Count_Cells_Std_norm'] ))
scatter_plot.add_trace(go.Scatter(x=combined_moa_cellcount_df['MoA'], y=combined_moa_cellcount_df['average_precision_act'],hovertext=[combined_moa_cellcount_df['Metadata_Count_Cells_act']], mode='markers', name = 'Phalloidin 400LS', marker_size =combined_moa_cellcount_df['Metadata_Count_Cells_act_norm']))
scatter_plot.update_layout(height=1000,width=1500, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision', legend=dict(yanchor="top",y=0.99,xanchor="left",x=0.01))
scatter_plot.update_xaxes(tickangle=90, categoryorder='total ascending')
scatter_plot.show('notebook')
The negative values indicate the better performance of Phalloidin 400LS
fig = go.Figure()
fig.add_trace(go.Scatter(x=combined_moa_df['MoA'], y=combined_moa_df['std_vs_act'],mode='markers', hovertext=combined_moa_df['Common Name']))
fig.update_layout(height=1000,width=1700, font_family='Arial', font=dict(size=14, color='Black'))
fig.update_yaxes(title='Difference in <br> Mean average precision')
fig.update_xaxes(categoryorder='total ascending')
fig.show('notebook')
combined_moa_cellcount_trmt_df = pd.read_csv('copairs_csv\\PrecisionValues_with_MoA_allplates_cellcount_Negcon_wrt_trmt.csv')
actin_fig_1 = px.scatter(combined_moa_cellcount_trmt_df, x =combined_moa_cellcount_trmt_df['average_precision_std'], y=combined_moa_cellcount_trmt_df['average_precision_act'],labels={'average_precision_std':'Mean Average Precision - Standard CellPainting dyes', 'average_precision_act':'Mean Average Preicison - <br> Phalloidin 400LS (long-stoke shifted)'}, color=combined_moa_cellcount_trmt_df['MoA'])
actin_fig_1.update_layout(legend=dict(orientation="h"), height=800, width=1000)
actin_fig_1.show('notebook')
combined_box_plot_1 = go.Figure()
combined_box_plot_1.add_trace(go.Box(y=combined_moa_cellcount_trmt_df['average_precision_std'], name = 'Standard Cellpainting dyes', boxpoints='all', hovertext=combined_moa_cellcount_trmt_df['MoA']+'-'+ combined_moa_cellcount_trmt_df['Common Name']))
combined_box_plot_1.add_trace(go.Box(y=combined_moa_cellcount_trmt_df['average_precision_act'], name = 'Phalloidin 400LS', boxpoints='all', hovertext=combined_moa_cellcount_trmt_df['MoA']+'-'+ combined_moa_cellcount_trmt_df['Common Name']))
combined_box_plot_1.update_layout(height=800,width=1000, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision')
combined_box_plot_1.show('notebook')
The size of the markers represent the average number of cells present in the replicates. The number of cells were normalized by dividing the actual number by 100 for easier plotting.
scatter_plot_1 = go.Figure()
scatter_plot_1.add_trace(go.Scatter(x=combined_moa_cellcount_trmt_df['MoA'], y=combined_moa_cellcount_trmt_df['average_precision_std'],hovertext=combined_moa_cellcount_trmt_df['Common Name'], mode='markers', name = 'Standard Cellpainting dyes', marker_size=combined_moa_cellcount_trmt_df['Metadata_Count_Cells_Std_norm']))
scatter_plot_1.add_trace(go.Scatter(x=combined_moa_cellcount_trmt_df['MoA'], y=combined_moa_cellcount_trmt_df['average_precision_act'],hovertext=combined_moa_cellcount_trmt_df['Common Name'], mode='markers', name = 'Phalloidin 400LS', marker_size=combined_moa_cellcount_trmt_df['Metadata_Count_Cells_act_norm']))
scatter_plot_1.update_layout(height=1000,width=1500, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision', legend=dict(yanchor="top",y=0.99,xanchor="left",x=0.01))
scatter_plot_1.update_xaxes(tickangle=90, categoryorder='total ascending')
scatter_plot_1.show('notebook')
The negative values indicate the better performance of Phalloidin 400LS
fig = go.Figure()
fig.add_trace(go.Scatter(x=combined_moa_cellcount_trmt_df['MoA'], y=combined_moa_cellcount_trmt_df['std_vs_act'],mode='markers', hovertext=combined_moa_cellcount_trmt_df['Common Name']))
fig.update_layout(height=1000,width=1700, font_family='Arial', font=dict(size=14, color='Black'))
fig.update_yaxes(title='Difference in <br> Mean average precision')
fig.update_xaxes(categoryorder='total ascending')
fig.show('notebook')